See Step 5 in the Python script to answer the following questions: In general, how is a multiple linear regression model used to predict the response variable using predictor variables? . What is the equation for your model? What are the results of the overall F-test? Summarize all important steps of this hypothesis test. This includes: a. Null Hypothesis (statistical notation and its description in words) b. Alternative Hypothesis (statistical notation and its description in words) C. Level of Significance Report the test statistic and the P-value in a formatted table as shown below: Table 2: Hypothesis Test for the Overall F-Test Statistic Value Test Statistic X.XX *Round off to 2 decimal places. P-value X.XXXX *Round off to 4 decimal places. e. Conclusion of the hypothesis test and its interpretation based on the P-value . Based on the results of the overall F-test, is at least one of the predictors statistically significant in predicting the total number of wins in the season? What are the results of individual t-tests for the parameters of each predictor variable? Is each of the predictor variables statistically significant based on its P-value? Use a 1% level of significance. . Report and interpret the coefficient of determination. What is the predicted total number of wins in a regular season for a team that is averaging 75 points per game with a relative skill level of 1350? What is the predicted total number of wins in a regular season for a team that is averaging 100 points per game with an average relative skill level of 1600?After you are done with your edits, click the block of code below and hit the Run button above. In [7]: import statsmodels . formula. api as smf # Multiple Regression # - -- - TODO: make your edits here - -- model2 = smf . ols( ' total_wins ~ avg_pts + avg_elo_n', nba_wins_df) . fit() print (model2 . summary ( ) ) OLS Regression Results Dep. Variable: total_wins R- squared: 0. 837 Model: OLS Adj . R-squared: 0. 837 Method : Least Squares F-statistic: 1580. Date: Sun, 14 Aug 2022 Prob (F-statistic) : 4. 41e-243 Time : 17 :22: 13 Log-Likelihood: - 1904.6 No. Observations: 618 AIC: 3815. Of Residuals: 615 BIC: 3829. Df Model : 2 Covariance Type: nonrobust coef std err P > | t [0 . 025 0.975] Intercept -152. 5736 4.500 - 33. 903 0. 090 -161 . 411 -143.736 avg_pts 9. 3497 0. 048 7. 297 0. 000 0 . 256 0. 444 avg_elo_n 0. 1055 0. 002 47.952 0. 000 0. 101 0. 110 Omnibus : 89 . 087 Durbin -Watson : 1. 203 Prob (Omnibus ) : 0.090 Jarque -Bera (JB) : 160.540 Skew: -0. 869 Prob ( JB ) : 1. 38e-35 Kurtosis : 4.793 Cond. No. 3. 19e+04 Warnings : [1] Standard Errors assume that the covariance matrix of the errors is correctly specified. [2] The condition number is large, 3. 19e+04. This might indicate that there are strong multicollinearity or other numerical problems